A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.

ABSTRACT: In drug development, managing interactions such as drug-drug, drug-disease, and drug-nutrient is critical for ensuring the safety and efficacy of pharmacological treatments. These interactions often overlap, forming a complex, interconnected landscape that necessitates accurate prediction to improve patient outcomes and support evidence-based care. Recent advances in artificial intelligence (AI), powered by large-scale datasets (e.g., DrugBank, TWOSIDES, SIDER), have significantly enhanced interaction prediction. Machine learning, deep learning, and graph-based models show great promise, but challenges persist, including data imbalance, noisy sources, Limited explainability, and underrepresentation of certain types of interactions. This systematic review of 147 studies (2018-2024) is the f

SUBMITTER: Marzouk NH 

PROVIDER: S-EPMC12447627 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature